Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data

Maner, William L.; Garfield, Robert E.

doi:10.1007/s10439-006-9248-8

Cited by 117 publications

(135 citation statements)

References 25 publications

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“…Artificial Neural Networks (ANN) have been used effectively in research to classify term and preterm delivery records [11], [12]. For example, Alaskar et al [13] proposed a neural network that builds on the back-propagation network, called the self-organized layer inspired by immune algorithm (SONIA), to classify both term and preterm labour using EHG signals.…”

Section: Related Studiesmentioning

confidence: 99%

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

Idowu

Fergus

Hussain

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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-Worldwide the rate of preterm birth is increasing, which presents significant health, developmental and economic problems. Current methods for predicting preterm births at an early stage are inadequate. Yet, there has been increasing evidence that the analysis of uterine electrical signals, from the abdominal surface, could provide an independent and easy way to diagnose true labour and predict preterm delivery. This analysis provides a heavy focus on the use of advanced machine learning techniques and Electrohysterography (EHG) signal processing. Most EHG studies have focused on true labour detection, in the window of around seven days before labour. However, this paper focuses on using such EHG signals to detect preterm births. In achieving this, the study uses an open dataset containing 262 records for women who delivered at term and 38 who delivered prematurely. The synthetic minority oversampling technique is utilized to overcome the issue with imbalanced datasets to produce a dataset containing 262 term records and 262 preterm records. Six different artificial neural networks were used to detect term and preterm records. The results show that the best performing classifier was the LMNC with 96% sensitivity, 92% specificity, 95% AUC and 6% mean error.

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Section: Related Studiesmentioning

confidence: 99%

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

Idowu

Fergus

Hussain

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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“…That being the case, it stands to reason that the ANN-EMG predictor could prove to have critical functionality when applied to species whose currently available predictors are inadequate, and this is precisely the present condition in the clinical forecasting of labor and delivery in humans. ANN with EMG data has been used in humans and it effectively classifies term/preterm labor/ nonlabor patients (15 ). The ANN-EMG method offers higher sensitivity and postivie predictive values over other common clinical predictors (such as fetal fibronectin or cervical ultrasound).…”

Section: Commentmentioning

confidence: 99%

Identification of term and preterm labor in rats using artificial neural networks on uterine electromyography signals

Shi

Maner

MacKay

et al. 2008

American Journal of Obstetrics and Gynecology

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CONDENSATION-Term or preterm delivery in rats can be effectively predicted using artificial neural network analysis of uterine EMG data.Objective-To use artificial neural networks (ANN) on uterine electromyography (EMG) data to identify term and preterm labor in rats.Study Design-Controls (G1:N=4) and preterm labor models (G2:N=4, treated with onapristone) were used. Uterine EMG and intrauterine pressure (IUP) variables were measured by implanted telemetric devices. For each time-point assessed, either a "labor-event" or "non-labor-event" was first assigned using visual and other means. 112 total labor and non-labor events were observed. ANN was then used with EMG and IUP parameters to attempt algorithmic, objective identification for time of labor in each group.Results-For G1, 8/8 (100%) of labor events and 44/44 (100%) of non-labor events were correctly identified by the ANN. For G2, 22/24 (92%) of labor events and 31/36 (86%) of non-labor events were correctly determined by the ANN.Conclusion-ANN can effectively predict term and preterm labor during pregnancy using uterine EMG and IUP variables.

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“…This permitted to suppress most of the noise introduced by the respiration, the maternal electrocardiogram, and the abdominal electromyogram [8]. The filtered signals could therefore be downsampled from 1024 to 16 Hz without introducing aliasing and reducing significantly the computational complexity of the following analysis.…”

Section: B Data Preprocessingmentioning

confidence: 99%

Electrohysterographic conduction velocity estimation

Mischi

Rabotti

Vosters

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Monitoring and analysis of the fetal-heart and the uterine-muscle activity, referred to as electrohysterogram (EHG), is essential to permit timely treatment during pregnancy. While remarkable progress is reported for monitoring of the fetal cardiac activity, the EHG measurement and interpretation remains challenging, and limited knowledge is available on the underlying physiological processes. In particular, little attention has been paid to the analysis of the EHG propagation, whose characteristics might indicate the presence of coordinated uterine contractions leading to intrauterine pressure increase. Therefore, this study focuses for the first time on the noninvasive estimation of the conduction velocity of EHG action potentials by means of multichannel EHG recording and surface high-density electrodes. A maximum likelihood algorithm, initially proposed for skeletal-muscle electromyography, is modified for the required EHG analysis. The use of clustering and weighting is introduced to deal with poor signal similarity between different channels. The presented methods were evaluated by specific simulations, proving the combination of weighting and clustering to be the most accurate method. A preliminary EHG measurement during labor confirmed the feasibility of the method. An extensive clinical validation will however be necessary to optimize the method and assess the relevance of the EHG conduction velocity for pregnancy monitoring.

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Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data

Abstract: ANNs, used with uterine EMG data, can effectively classify term/preterm labor/non-labor patients.

Cited by 117 publications

References 25 publications

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

Identification of term and preterm labor in rats using artificial neural networks on uterine electromyography signals

Electrohysterographic conduction velocity estimation

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